There are lack of researches in the offensive language for Arabic research community, for instance, [1]} introduced k-means for violence utterance on twitter. MADIMARA has been used to extract morphological features as well as they used TF-IDF to represent dataset on the vector space model. [2]} presented system to detect hate speech using lexical features and a linear SVM classifier depending on n-grams. Similarity, [3]} introduced SVM classifier trained on word-level features. N-grams and stemming used as features. [4]} proposed L-HSAB the first dataset for hate speech and abusive language. The dataset collected from twitter API focusing on Syrian and Lebanese tweets rich of toxic utterance. The dataset has been trained on Naive Bayes classifier.
